Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
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Knowledge search

We have gotten to a point where, in some cases, we don't even realize we are using artificial intelligence. A sign that a technology or product is good is when we don't necessarily stop to think how it's doing what it is doing. A perfect example of this is Google Search. The product has become ubiquitous in our lives and we don't realize how much it relies on artificial intelligence to produce its amazing results. From its Google Suggest technology to its constant improvement of the relevancy of its results, AI is deeply embedded in its search process.

Early in 2015, as was reported by Bloomberg, Google began using a deep learning system called RankBrain to assist in generating search query responses. The Bloomberg article describes RankBrain as follows:

"RankBrain uses artificial intelligence to embed vast amounts of written language into mathematical entities — called vectors — that the computer can understand. If RankBrain sees a word or phrase it isn't familiar with, the machine can make a guess as to what words or phrases might have a similar meaning and filter the result accordingly, making it more effective at handling never-before-seen search queries."
— Clark, Jack [2]

As of the last report, RankBrain plays a role in a large percentage of the billions of Google Search queries. As one can imagine, the company is tight lipped about how exactly RankBrain works, and furthermore even Google might have a hard time explaining how it works. You see, this is one of the dilemmas of deep learning. In many cases, it can provide highly accurate results, but deep learning algorithms are usually hard to understand in terms of why an individual answer was given. Rule-based systems and even other machine learning models (such as Random Forest) are much easier to interpret.

The lack of explainability of deep learning algorithms has major implications, including legal implications. Lately, Google and Facebook among others, have found themselves under the microscope to determine if their results are biased. In the future, legislators and regulators might require that these tech giants provide a justification for a certain result. If deep learning algorithms do not provide explainability, they might be forced to use other less accurate algorithms that do.

Initially, RankBrain only assisted in about 15 percent of Google queries, but now it is involved in almost all user queries.

However, if a query is a common query, or something that the algorithm understands, the RankBrain rank score is given little weight. If the query is one that the algorithm has not seen before or it does not know its meaning, RankBrain score is much more relevant.